Conference Transcript - DDOG 2026-09-10

Speaker 2

Everyone, welcome to the Datadog session where we are debating strong suits versus weak suits in pop culture and trivia. Hey, it's a real pleasure to have David, CFO of Datadog, on stage with me. David, really appreciate you making the time to be with us this morning. We started having a couple of really interesting conversations over dinner last night that I'd love to talk through a little bit with a broader audience. The first one is this idea of applying proprietary data to an SLM or an LLM. And Datadog has a really unique data set, if I think about the types of observability data that you've been collecting since the founding of the company. Tell us a little bit about what Datadog can do with that time series forecast and how you could apply it to the next product cycles in your business?

David Obstler, CFO

Great question. So we used to call it ML or correlation. So for a long time, data dogs, one of their strengths has been to be able to aggregate enough information around the signals of the functioning of client-facing applications and to be able to be somewhat predictive. And AI and models have provided a very unique opportunity. We recently made an acquisition of a company called Adaptive ML, which is a specialist in reinforcement learning around areas in IT management and observability that we cover. And that, with the data we have on observability and the functionality of applications and the research lab that we've created, we put out a model a while ago called Toto, but that's just the beginning of what we think will be a very strong competitive advantage, which is using AI and models, some of which will be open source, some of which may be the foundation companies, to own the data and produce models which are going to be predictive of issues around the functionality of applications, observability. And we're investing behind that. We talked about that last night, both in terms of people, the GPUs, the inference, et cetera. And that data is not public. That data is, Datadog has because of our position and size and observability, and we think that will deliver a lot of value to clients and also be a competitive advantage in the evolution of the platform.

Speaker 2

Let's stay on the advantages that it can give your clients. If I think about the holy grail in observability, there's this idea of automated site reliability engineering, and you have a little bit of that with the BITS product. So maybe just paint the vision for us when you talk to the founding team, where could a technology like Toto go over time?

David Obstler, CFO

The vision here is to produce more accurate and quicker real-time signals which can improve the functionality of the platform and go on the continuum to allow for auto-remediation or close to it. And that will be the speed of analyzing problems all the way towards having the platform able to act independently. That will be, when we get there, we're not there yet, will be a combination of the evolution of the platform. Our BITS product line is what is going to use these models and data to be predictive. And then clients in our vision will be able to make the choice of how much to auto-remediate to say, for this type of issue, the platform can auto-remediate. For this type, there'll be a suggestion, and then someone will have to press yes. And that has tremendous ramifications, both in terms of the speed and also the efficiency in human capital in this endeavor, and that's the vision of our founder, Ali, and where we're investing behind.

Speaker 2

The other interesting thread to pull on here is this idea of an inference economy. And what I mean by that is we're so early when you look at AI adoption and inference in particular at classic enterprises, classic Datadog customers, such that if you take a step back and say, well, we're at the very early stages of an inference cycle here, that could have really interesting implications for Datadog's growth rate, not just this year, but over a three-year timeframe from a structural standpoint. So talk us through that a little bit. What are you seeing at the typical customer? When a customer goes from zero inference to 1%, 3% of the OPEX budget going to inference, what are the implications for Datadog's opportunity at that customer?

David Obstler, CFO

Definitely. So Datadog is in the business of monitoring whatever can affect the functionality of an application. And the more complex it goes, there's more in it, the more opportunity to get revenue. And I think we talked about last night that early on, the first wave was essentially calling out to the model companies through APIs. So the first way to monetize, this is Datadog for AI, meaning it's just like Datadog's monitoring code, Datadog's monitoring CPUs, Datadog's monitoring databases. This is something else that we've begun to monetize through our agent observability. And although early on we're seeing thousands of customers use this, and we're starting to get the revenue streams from it, and what we believe is happening is if we look at some of the other metrics of building out AI-enabled applications through a combination of the outside models, open source, and inference, is that creates a whole other set of things to monitor that we're starting to see in our BITS products, in our MCP server calls, and other indications, a lot of activity, which, again, will increase the complexity and we believe create a more importance in observability, more to monitor, and more velocity in application creation, which should all benefit Datadog.

Speaker 2

There is a sovereign thread in here around, We've had actually a number of companies. We think about Satya Nadella talking about frontier ecosystems, Core Wave talking about more customers wanting to run their entire training, post-training inference tax and how we even at Goldman, we've been talking just this morning about proprietary data at Goldman that can be leveraged with the model. Are you starting to see what is in your customer base? Are you starting to see that shift occur between, hey, we're just going to use the frontier lab, we're going to outbound APIs, versus actually we want more of that stack to be in-house and we're going to run more proprietary processes internally.

David Obstler, CFO

We're starting to see that in the metric I mentioned on usage, but also at Datadog. So we talked earlier about our models. So we early on used the outside models and the foundational models, and we put that in the platform. And similar to what you're talking about at Goldman, we own the data. We're the experts in observability. We have the ability to create from a functionality and also a cost models that we most perform in, and we're starting to invest behind it, which I think is going to result in reallocation of the cost of our token costs towards our proprietary models. Early on, it takes investment. It takes Lyft to do that, but just like you're talking about at Goldman, we're seeing that in the metrics we're seeing in our observability and also what Datadog's doing in its own research lab. Agree with you. Yeah.

Speaker 2

Let me ask you about what some of your more sophisticated customers are doing, including some of the Frontier Lab wins that you talk about in your AI native cohort. There is a tendency from the outside in we'll see reported ARR numbers from Frontier Labs, and we'll extrapolate that and we'll say, well, Datadog, depending on what the contract looks like, should have a really interesting correlation with what's reported publicly. Now, in reality, it's a little bit more complicated than that. So I know you can't speak on any specific customer. Talk about that cohort in general. What are you seeing in terms of usage patterns, and how does that map back to the Datadog wallet?

David Obstler, CFO

It's an important thing. We basically have invented this AI native and non-AI native. Fundamentally, the AI natives are cloud native companies. what they have in common is they're investing in modern applications, they're experiencing an accelerated demand environment, and they don't have legacy infrastructure. So essentially, and their whole business is delivering this functionality in the models. So what we're seeing is, one, we're seeing strong growth in workloads. Two, we're seeing a pattern of outsourcing to data dogs. So a number of these companies started out doing – there's always a tradeoff between do-it-yourself or using a data dog. Now, these companies have a huge R&D pipeline, and what they're increasingly realizing is it's not efficient to spend that, building your own observability, use data dogs. So we're seeing a movement from open source or initial efforts or cobbling it together towards data dog. We're seeing a use of, because they're newer customers, a use of many of our products, the platform. So we're seeing the average use of products to be at the higher end. But the main thing we're seeing is accelerated growth. They're very similar to other modern software companies that are experiencing demand cycle. Now, most of this is in production, inference and production. What we're also seeing, which we talked about on our Ernie's call, which really struck us, we originally said, well, Datadog's really the production company, inference and production. We're really not going to see a lot of demand on training. But then what happened to us is some of the larger hyperscalers and other companies came to us and said, we want to use you for training or post-training workloads, which we talked about. And that was a surprise to us. But then we thought back and we said, you know what's happening? The speed of this is such that the pre-production or, you know, late training into production is starting to merge, which created this revenue stream. We don't believe we have the evidence to say that we're going to be the training company, but we're also seeing in some of these AI native companies the use of Datadog in training, which has been an additional source of revenues.

Speaker 2

The question we get, and you address this every earnings call with your comments on large customer, how do you think about the risk that you will have more large customers over time that will say, look, we're willing to take on the pain of running a more complex stack or more DIY stack because the cost savings make it worth it?

David Obstler, CFO

I think when you step back and you look at our gross retention means are you staying on Datadog or not, It tends to be, we said, in the upper 90s with larger enterprise being at the high end of that. So we don't retain every customer, but the vast majority of customers have been making the decision to use the Datadog platform. And, in fact, over time, as I mentioned, the trend has been that it is economic, priority-oriented to, instead of building an observability platform yourself, to put your scarce R&D dollars into your product and your business. So that's been the weight of it. But it isn't the case all the time, and it also isn't 100% or 0%. Many customers are doing a combination, and what we said all along, which is from our largest customer, is that we have commitment contracts, and that customer is growing rapidly and spending above the commitment, but we know that in certain customers, there'll be sort of a tradeoff between, and what we said in our last earnings call was that that customer renewed with us, staying with Datadog, extending the contract, and the usage there has moved around relative to what they're doing. Sometimes it makes sense to hook up a database that doesn't have to be or a metric store doesn't have to be real-time with Datadog and put the other work that has to be real-time. So we had some volatility, and what we decided to do, given the investor focus on this, is to de-risk our guidance by putting in our guidance only the commitment. It can't go below the commitment. But we've always said that's a fringe case. For the most part, our business is not about outsourcing the hyperscalers' observability. We're not a concentrated company. So in a weighted, and you see that in our gross retention, the vast, vast majority of our customer base, given the gross retentions we're talking about, have stayed with Datadog and the fact that the net retention has been increasing means that they've been putting more of their workloads on Datadog rather than less.

Speaker 2

Let's stay on the vast majority of the customer base in that case. There are some really interesting comparisons that you and I have talked about about the 2021, 2022, 2023 optimization cycle and how your customers have actually learned from that, and you as a company have learned from that too. So when we have investors say, look, this category by definition has waves to it of optimization versus growth, could we be entering a phase of optimization? How do you know we're not about to enter a phase of optimization? Tell us a little bit about the forecasting tools that you use and how you think this time is different.

David Obstler, CFO

I think things are pretty different than they were when the bubble burst. So first of all, we were growing at 70%. We're growing well, but we're not growing at 70. There was that, you know, zero interest rate, growth at all costs, and we're not in that market. We also had a much higher percentage of these cloud natives than we do now. So that's a bit of a different environment. We always will have a yin and yang of growth and optimization in cloud software. It's really the weighted average of that that matters. You can see that in the expanding net retentions that were net-net in a growth environment. I would say, to your point, we also, I think, learned ourselves. First of all, we're much bigger. We're much more diversified in all ways, geographically, type of customers. Our highly growing AI natives is a very important factor because those are some of the progressive customers, and that's a great sign that they're adopting us. but it's much smaller than that cloud native. And I think our customer base has not forgotten what happened and has, if you look at the net retention, acted more responsibly. We also have built out a number of functions to provide information transparency to our customers, work with them, account managers, you know, skew and non-skew types of arrangements where we help our clients use it, use the product in the right way. So I think we've gotten a lot better in helping our clients. We also have things with contract extension, volume pricing, a number of things to ameliorate the risk. So I think you're right on. It's that the end market has learned. We are much more diversified, and we've learned, I think, how to be better in helping our clients to have long-term growth with us.

Speaker 2

One of my favorite examples of this is actually the announcement on infinite cardinality.

David Obstler, CFO

Good point.

Speaker 2

And this could be a much longer discussion, but maybe give us the shorter version of why infinite cardinality addresses one of the budget pushbacks that you would have gotten from customers.

David Obstler, CFO

Definitely. So I'll focus on infinite cardinality, but then I'll also mention it's one of a portfolio of technology innovations where we have met the customer where they are, flex logs, frozen logs, metrics without limits.

Speaker 2

I have another five questions on that.

David Obstler, CFO

Yes, and it's a really good question. And so, basically, what we've been, I think, much smarter at is understanding how the client is using the product. So, cardinality is the sampling and the use of the data. And what we came to understand is in some examples of how a customer is using it, it's much better to, I would say, curate or figure out how all of the metrics don't flow in and get processed, but they get sorted before. I'm using very simple language. And then that is good for the customer because they get more value in their metrics. That's also good for us because it doesn't help us to have metrics or even logs that flow in and are costly to us. So there are many, many examples. I think it's a very good point of how we've been evolving the platform to meet the client at the value point. And what we've been doing is proactively converting those customers that can benefit from infinite cardinality to our SKU, which has essentially been retentive and also margin-enhancing for us, given the weight on our platform of large cardinality of our metrics.

Speaker 2

The other pricing announcement, it's not really a pricing announcement, but I think we could frame it as driving value to customers, federated logs.

David Obstler, CFO

Federated logs, yeah.

Speaker 2

This is really interesting because it actually sounds like you can bring a more heterogeneous architecture into how customers organize their logs, for example, with ClickHouse. So talk about how federated logs works.

David Obstler, CFO

Yeah, talk about federated logs. It's in a group of innovations where we used to have all the data had to flow into Datadog to be used, but we know that's not efficient. So federated logs, data observability pipelines, bring your own cloud. What this means in the case of federated logs is that we know that not all logs have to flow into Datadog to enhance the platform. that we can link into and logs can be stored. In this case, it was ClickHouse and Databricks. So what we're doing is we understand certainly we want to be able to have more data available in the platform without having to port all of your data into Datadog. That we've proven and we've gotten feedback from clients enhances the value of the platform, eliminating one of the objections is I have to bring all my data into Datadog even though I'm storing it elsewhere. And this is a better way to use Datadog, to have what flows in to be the most important but to leave what doesn't need to flow in there. And essentially what that's doing is that's exposing our platform to more data that is not in Datadog enhancing the value of the platform. And that, you could say that's also what we're doing with Bring Your Own Cloud. There are many examples where it might make sense for a client, either because of the volume, regulatory reasons or otherwise, to leave the logs or the information on the client side, but use the Datadog analytics. So the federated logs are a good example of one of a number where we're opening up the Datadog platform, and we believe that will be enhancing value to clients, improving the cost structure, and opening us up to business that we would not have had otherwise.

Speaker 2

This word that you're using on opening I think is important because one could argue, okay, so if I structure my logs differently, less budget goes to Datadog because now part of my observability budget on the log site is going to ClickHouse and Databricks. Talk about the point on, well, actually, that tradeoff is worth it.

David Obstler, CFO

Yeah, we've analyzed that. We actually thought, okay, we want the logs that need to be accessed there to be in Datadog, but we want the platform to be able to use as a central point. The more we have our users centralized on Datadog, We've proven that we get higher average revenue per customer. So to close Datadog off and force them to go to another platform is not as optimal as staying in Datadog all day long and using the data efficiently. We actually, getting back to how we've evolved, we actually go to clients and we say, you know, you set this wrong. You're flowing too much data into Datadog that you don't need for this purpose, and it creates a much better long-term value of customer and also in many cases is margin-enhancing for us because to store all that data in Datadog that is never used also is a burden on our system.

Speaker 2

Every two to three quarters, we all have to ask you about some new competitor in this space, whether it's an AI native or... So tell us how you would respond to the premise that this time is different, meaning, well, because there's an architectural shift happening, because AI involves different scalability of data, therefore the observability companies founded 5, 10, 15 years ago are not the same ones that will benefit from a true AI-native observability stack 3, 5, 10 years from now.

David Obstler, CFO

Yeah, a couple different questions. So I'm eight years into data, but I'll be in public for six-plus years. So there are many, many companies outside of observability that have tried to do this, and there's one that's been speaking about it now. It's easier said than done. So very few have been able to, or none, I would say, have been able to organically grow an integrated platform where you can see everything that's going on. A lot of the attempts have been through acquisition, platforms that aren't integrated together. So, essentially, there's been a lot of companies that have tried to do this. They have not succeeded. Now, this is really up to us. The question is, there are point solutions that are out there. Generally, there's been a preference for a single pane of glass and everything knitted together. So, that's a big lift. But it is important, very important for Datadog to not rest on that, but to basically out-innovate all of these emerging companies by modernizing our own platform. This is AI for Datadog and Datadog for AI. But in this case, I think we're essentially investing behind making sure that we are the leading innovator here so that the balance of trade between modern point solutions and the platform of Datadog is weighted in our favor. And that's why we're investing significantly. Now, I think it is a very big competitive advantage. Datadog, and if Ollie were sitting here, he would say the thing that investors should think about is Datadog has consistently invested 30% of its revenues in R&D. That's over a billion dollars. That is out investing everybody else in the market. And why that's important is exactly your point. We have to get there first and more scalably and more knitted together in the modern platform than the point solutions that are emerging. And I think we've been pretty successful in that, but we're not resting our laurels. We have to continue doing that.

Speaker 2

I'd like to spend a couple of minutes on a secular trend that has been core to Datadog since day one, which is the move to cloud. So there are two things happening that we've picked up in parallel. One is you're investing more on enterprise sales. and two is we have a number of enterprises telling us that migrating to cloud is more of a strategic imperative today than it was two years ago or even five years ago. Talk to us about how those two trends intersect. Is there a biggest – is the share of market and observability that's going towards cloud taking another step function up right at the same time as you're doubling down on the go-to-market?

David Obstler, CFO

Definitely. I think that's very true. So despite the fact that cloud's been around, there are, one, there are so much legacy infrastructure. The research companies say that somewhere upper 20%, 30% of applications are in the cloud. And there are tons of enterprises that, you'd be shocked, are so immature, meaning they really haven't even started their first material projects. But the fact that any time there's been change of technology, of which AI is a major one, the impetus or the urgency of both modernizing applications and putting them in the cloud is enhanced. And we're seeing that. That's some of the reasons why we're seeing the non-AI business accelerate. At the same time, it's not going to happen by itself. our bottoms-up selling, that's very effective, has to increasingly be complemented by top-down and between that. And I think it's been really important for us over the years to build out our enterprise sales team in order to catch that wave. And some of the good examples of that are we have a key accounts group. We used to, I would say, bank on the fact that our sales cycles were going to be discreet, our commission plans were done that way. But I think what we've learned is some of these enterprises, this is a very long sales cycle. And you can't have an enterprise salesperson covering 10 accounts. They're covering one or two accounts where the IT budget and conversion in the cloud can be in the tens of millions. And we stick this person on that account, and they're going after all the projects there. And this is the evolution. We didn't used to be like that. So I think in many, many ways, whether it be key accounts, named and major accounts, the partners, our investment in channels, our investment in data centers, all of this is aimed at exactly what you're saying is to follow that evolution of the enterprises that are still very early in their migration and be there at the time they do their migration projects. And when you look, if you go back through our earnings and you look at what we're announcing and you see the industry, banking, insurance, automotive, manufacturing, airlines, you see evidence of this happening and it would not be possible if you weren't building out this enterprise sales team aggressively on a global basis at the same time this transformation is happening.

Speaker 2

Innovation and R&D has been a consistent theme over the last 30 minutes. Give us a couple of examples. When you reflect on how your team operates, how you see the R&D team operating within Datadog, give us a couple of examples on how things have changed over the last year.

David Obstler, CFO

Yeah. Well, one thing I think is very important to think about is the value of the platform. The value of the platform in doing this has meant that it has been very efficient enough for us to build functionality on top. So one thing I think is we, as the platforms got, we talked about a lot of the innovations that have happened in the platform. That's accelerated, I think, the new product and has enabled us in a very time-oriented way to get innovation. What's happening now is a very important innovation. We are working on the balance between AI and coding tools and human capital, and I think in our history, we've invested very rapidly in human capital, and we believe we'll continue to have to do that, but we are starting to see signs of real efficiency in terms of using coding agents to improve the velocity, and it's still early. We have A-B teams. We have a team that has a lot of people and less access to coding, and we have teams that are accessed to more of the coding tools. And we're experimenting with what the, you know, cross-pollination, what the A-B testing is. So I think that's a very important evolution. Another important evolution is the research lab. When you think about what we talked about first, we are investing significantly behind our own models and the proprietary nature of that that is starting to result in the weight of the R&D budget. But I think there's still a lot of consistency in that we still have our eyes firmly on the customer, firmly on what are the product innovations they demand, and we're still very commercial people in that we have the feedback loop always putting that with the revenues and the SKUs we create. So I think that's just some of the evolution of the R&D at Datadog.

Speaker 2

I think that's an excellent place to leave things. Please join me in thanking David for his time.

David Obstler, CFO

Thank you very much, everybody. Thanks. Thank you.